In fintech, AI is used for chatbot development, process automation, and emerging technologies like Robo-advisors and virtual assistants. These applications have brought about significant changes, including increased efficiency, reduced errors, and improved customer experience. AI technologies like these use algorithms to analyze financial data and make investment recommendations. Virtual assistants are AI programs that can complete tasks and provide information for customers. For example, a virtual assistant may be able to answer questions about a financial product or service and assist with the purchase process.
So, it would not be wrong to say that the use of AI in fintech has brought about significant changes, including increased efficiency, reduced errors, and improved customer experience.
However, fintech companies should consider several factors when incorporating AI in the business, such as seven primary factors when using agile methodology to realize the potential of AI. In a telephonic interview Sahil Khullar, an agile project manager who has been at the forefront of leading and managing the development of financial technology products or services using the Agile method, to deliver high-quality software that meets the needs of businesses and their customers, stated that six primary factors need to be taken into consideration for fintech companies to successfully incorporate AI using agile methodology into their business and realize the full potential of this technology. These factors include:
Regulatory compliance:
Fin Tech companies must ensure that they are compliant with data protection laws, such as the General Data Protection Regulation (GDPR) in the European Union, which sets out rules for the collection, use, and protection of personal data. According to Khullar, data protection has always been an issue when it comes to the integration of AI as AI technologies are developed using real-life historic data, which makes it possible for these technologies to predict trends. For example, a fintech company that uses AI to provide investment advice must ensure that its algorithms are transparent and do not engage in activities such as insider trading or market manipulation," he explains.
Ethical considerations:
AI in finance can lead to ethical issues such as bias and job displacement. Bias in training data can result in discriminatory decisions. Fintech companies should address these concerns by implementing fairness and bias prevention measures, such as using diverse data, and planning for potential negative effects of automation on their workforce. According to a Forbes article, approximately 80% of mortgages were denied to people with African American names.This was because the data used to train the AI model included a disproportionate number of home loans that were approved or denied based on factors such as race or gender. The bank was forced to overhaul its AI system and implement new processes to ensure that it was fair and unbiased.
"Fintech companies should address ethical concerns with AI, including bias and job displacement, by implementing fairness measures and considering potential workforce impacts," says Khullar, who has been helping companies develop AI technologies for their fintech companies using the Agile method.
Data privacy:
Fintech companies should have strong data privacy policies and be transparent about data collection. Violations can result in fines and harm to reputation. Their use of AI should not harm consumers or engage in deceptive practices, such as discriminatory lending decisions. " Fintech companies must also ensure that their use of AI does not negatively impact consumers or engage in deceptive or unfair practices. For example, a fintech company that uses AI to make lending decisions must ensure that its algorithms do not discriminate against certain groups of people or make lending decisions that are not based on objective criteria," said Khullar, who is currently working as the Senior Principal Consultant for a Fintech.
Security:
Fintech companies should also ensure that their AI systems are secure and that they have robust cybersecurity measures in place. This is particularly important given the sensitive nature of financial data and data breaches could severely affect not only the company but also its customers. For instance, the Equifax data breach that is considered to be one of the ten largest data breaches in the finance sector. The Equifax data breach was caused by several security flaws, including a failure to patch a known vulnerability, a lack of segmentation in their system, the use of plaintext passwords, and an expired encryption certificate. As a result, the personal and financial information of over 40% of the US population, including names, dates of birth, social security numbers, driver's license numbers, and credit card numbers, was potentially compromised. The company was fined $700 million for the breach.
Cost:
AI implementation can be expensive and fintech companies should carefully consider costs and benefits before making a decision. Ongoing costs for maintaining and updating AI systems should also be considered. According to Khullar, the cost of implementing AI for a small fintech company can vary greatly and depends on the specific AI solutions, the complexity of the project, and resources and expertise available within the company.
Khullar points out that by scaling agile and utilizing lean budgeting, could be a solution to this problem. "In this type of budgeting Chatbots, anything else AI related development & implementation would fall under one value stream that organization could call Artificial Intelligence Technology Initiative. This way, chatbot would not have seperate budget from some other AI technology development and implementation and risk of losing budget while in the middle of the work."
Development & Integration with existing systems:
According to Khullar, "Fintech companies should also consider how their AI systems development should take place using agile methodology and deliver value incrementally in smaller pieces by focusing on establishing Minimum Viable Product (MVP) that focuses on prioritized list of top minimum features necessary to develop in order for AI product to be deemed successful by customers or internal stakeholders". Furthermore, how it would integrate with their existing systems and processes. Not only this, but also they should ensure that their AI systems are compatible with their existing infrastructure and that they have a plan in place for transitioning to the new technology. By scaling up agile and establishing quarterly planning across the company domain and subsequently program board, it could be revealed the dependencies among different teams working together towards integration, thereby bringing everyone on the same page, promoting transparency
Conclusion:
AI has greatly impacted the fintech industry through chatbot development, process automation, and emerging technologies like robot-advisors and virtual assistants. Fintech companies should consider regulatory compliance, data privacy and security, ethical considerations, development and integration with existing systems, costs and benefits, and the need for skilled personnel when incorporating AI into their business using agile. The cost of implementing AI for a small fintech company can vary greatly and depends on the specific AI solutions, the complexity of the project, and the resources and expertise available. Fintech companies may also need to invest in change management, training and development to ensure they have the necessary talent to implement and manage AI systems.
